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Business Management Research and Innovation Research Groups Artificial intelligence and other IT applications in economic

Artificial intelligence and other IT applications in economic

Our ambition is to integrate innovative hardware abilities to the solving of economic problems.
 
Topicality and originality of the research:
There is a number of artificial intelligence algorithms such as neural networks, genetic algorithms, fuzzy logic and agents systems. However, the application on the economics is not very widespread.  Artificial intelligence is using for applications such as data fitting, pattern recognition, clustering, time-series prediction, and dynamic system modelling and control. In economic field there is a lot of problems that can be solved using these cognitive processes. The main concept is performing research and innovation actions by the synergy of artificial intelligence and economics.
Our team adapted Evolino RNN for forecasting of exchange market. Stochastic approach to the financial markets inspires us to create Evolino RNN ensemble, whose accuracy has improved significantly. The new methods of forecasting, new marketing strategies, portfolio theory have been tested by using Evolino RNN ensemble.
Currently our team attempts this forecasting model to adapt for the forecasting of individual investors. The rapid development of technology divides the world into two parts: those who are able to quickly master the technology or create them and people with less activity related to technological progress. The „idea to application” of artificial intelligence in the economic must bring out new innovative business opportunities and become „lab to market” idea. Innovative methods can be used in the public sector and in industry and will be able to better address societal challenge in new ways.
 
 
 
 
Phone +370 5 274 4861
Email nijole.maknickiene@vgtu.lt
Finance markets, investor sentiment, forecasting, prediction.

MEMBERS

Supervisor:
Dr Nijolė Maknickienė (CV and publications)
 
Researchers:
Prof Dr Jelena Stankevičienė (CV and publications)
Dr Algirdas Maknickas (CV and publications)
Dr Indrė Lapinskaitė (CV and publications)
 

MAIN MONOGRAPHS AND PUBLICATIONS

  1. Maknickienė, N.; Maknickas, A..2013 Financial market prediction system with Evolino neural network and delphi method. Journal of business economics and management. Vilnius : Technika. ISSN 1611-1699. Vol. 14, no. 2 (2013), p. 403-413
  2. Maknickienė, N.; Maknickas, A. 2016.Prediction capabilities of Evolino RNN ensembles. Computational Intelligence. Berlin : Springer International Publishing, 2016. ISSN 1860-949X, p. 473-485
  3.  Stankevičienė, J.; Maknickienė, N.; 2014. Maknickas, A.. Investigation of exchange market prediction model based on high-low daily data. The 8th international scientific conference "Business and Management 2014". Vilnius : Technika, 2014. ISSN 2029-4441, p. 320-327..
  4. Maknickienė, N.; Maknickas A.. 2012 Application of neural network for forecasting of exchange rates and forex trading. The 7th international scientific conference "Business and Management 2012". Vilnius : Technika, 2012. ISSN 2029-4441, p. 122-127.
  5.  Rutkauskas, A. V.; Maknickienė, N.; Maknickas, A. 2010. Modelling of the history and predictions of financial market time series using Evolino. The 6th International Scientific Conference Business and Management 2010: selected papers. May 13-14, 2010. Vol. 1. Vilnius : Technika. ISSN 2029-4441. 2010, p. 170-175.
  6. Maknickas, A.; Maknickienė, N.. 2015. Investment support system using the EVOLINO recurrent neural network ensemble. IJCCI 2015. Setubal : SCITEPRESS – Science and Technology Publications, Lda., 2015. ISBN 9789897581571, p. 138-145..
  7. Maknickienė, N.2013. Selection of orthogonal investment portfolio using evolino RNN trading model. Procedia - Social and Behavioral Sciences. The 2-dn International Scientific conference „Contemporary Issues in Business, Management and Education 2013". Amsterdam : Elsevier Science Ltd. ISSN 1877-0428. 2014, Vol. 110, p. 1158-1165.
  8. Maknickienė, N.; Maknickas, A. 2013 Investigation of prediction capabilities using RNN ensembles. IJCCI 2013. Setubal : SciTePress, 2013. ISBN 9789898565778, p. 391-395..
  9.  Maknickas, A.; Maknickienė, N. 2012. Influence of data orthogonality: on the accuracy and stability of financial market predictions. IJCCI 2012. Setubal : INSTICC, 2012. ISSN 1860-949X, p. 616-619.
  10. Kvietkauskienė, A.; Maknickienė, N.  2015. The use of investment portfolio orthogonality to secure investment against bank interventions. Journal of System and Management Sciences (JSMS). Yuenlong, Hong Kong : Asia Association of System and Management Research (AASMR). ISSN 1816-6075. Vol. 5, no. 3 (2015), p. 1-17

SCIENTIFIC ARTICLES

  1. Maknickienė, N.; Maknickas, A..2013 Financial market prediction system with Evolino neural network and delphi method. Journal of business economics and management. Vilnius : Technika. ISSN 1611-1699. Vol. 14, no. 2 (2013), p. 403-413.
  2. Kvietkauskienė, A.; Maknickienė, N.  2015. The use of investment portfolio orthogonality to secure investment against bank interventions. Journal of System and Management Sciences (JSMS). Yuenlong, Hong Kong : Asia Association of System and Management Research (AASMR). ISSN 1816-6075. Vol. 5, no. 3 (2015), p. 1-17.
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